Volume 9 Number 7 (Jul. 2014)
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JSW 2014 Vol.9(7): 1922-1929 ISSN: 1796-217X
doi: 10.4304/jsw.9.7.1922-1929

A Multi-Threshold Granulation Model for Incomplete Decision Tables

Renpu Li, Tao Yu, Chunjie Zhou, Hongbo Li
School of Information and Electrical Engineering, LuDong University, Yantai, China

Abstract—How to establish basic granules of knowledge is a fundamental issue for data mining from incomplete decision tables. In the existing methods, basic granules under similarity relation contain too many objects and disturb the later knowledge mining, while granules under limited similarity relation, although simplifying the granules through introducing a limited threshold on two objects satisfying similarity relation, still have problems such as high computation and low prediction precision. In this paper, a multi-threshold model is presented to establish basic knowledge units of incomplete decision table based on the idea of granular computing, comparison experiments on the new model with two existing models show that the new model is superior to the other models on prediction precision, time cost and attribute reduction.

Index Terms—Incomplete decision tables, similarity relation, granular computing, multi-threshold


Cite: Renpu Li, Tao Yu, Chunjie Zhou, Hongbo Li, "A Multi-Threshold Granulation Model for Incomplete Decision Tables," Journal of Software vol. 9, no. 7, pp. 1922-1929, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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